Information about Test

  1. Vanishing gradient problem

    aimlexchange.com/search/wiki/page/Vanishing_gradient_problem

    training artificial neural networks with gradient-based learning methods and backpropagation. In such methods, each of the neural network's weights receives

  2. Artificial intelligence

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    deep neural networks that contain many layers of non-linear hidden units and a very large output layer. Deep learning often uses convolutional neural networks

  3. Comparison gallery of image scaling algorithms

    aimlexchange.com/search/wiki/page/Comparison_gallery_of_image_scaling_algorithms

    Dengwen Zhou; Xiaoliu Shen. "Image Zooming Using Directional Cubic Convolution Interpolation". Retrieved 13 September 2015. Shaode Yu; Rongmao Li; Rui

  4. Feature learning

    aimlexchange.com/search/wiki/page/Feature_learning

    are learned using labeled input data. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised

  5. Data science

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    Artificial neural network Autoencoder Deep learning DeepDream Multilayer perceptron RNN LSTM GRU Restricted Boltzmann machine GAN SOM Convolutional neural network

  6. Word2vec

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    used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec

  7. History of artificial neural networks

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    artificial neural networks (ANN) began with Warren McCulloch and Walter Pitts (1943) who created a computational model for neural networks based on algorithms

  8. Darkforest

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    developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its

  9. DexNet

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    Dex-net is a robotic manipulator. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects. Dex-net was developed

  10. Supervised learning

    aimlexchange.com/search/wiki/page/Supervised_learning

    Geman, E. Bienenstock, and R. Doursat (1992). Neural networks and the bias/variance dilemma. Neural Computation 4, 1–58. G. James (2003) Variance and

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